Zhang, S.; Mio, A.; Cagnoni, M.; Zhu, M.; Cojocaru-Mirédin, O.; Wuttig, M.; Scheu, C.: Valence EELS investigation on GeSexTe1-x phase change material. EDGE 2017: Enhanced Data Generated by Electrons, 8th International Workshop on Electron Energy Loss Spectroscopy and Related Techniques, Okuma, Okinawa, Japan (2017)
Bueno Villoro, R.: Microstructure, thermal stability and defect phonon scattering in AgSbTe2 thermoelectrics. Master, Universitat Autònoma de Barcelona, Spain (2019)
Bueno Villoro, R.: Effect of the processing route on the microstructure of Ag18Sb29Te53 (AST) based thermoelectrics. Bachelor, Universitat Autònoma de Barcelona, Spain (2018)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…